In 2026, neural networks are achieving unprecedented efficiency, multimodal integration, and workflow comprehension, yet benchmarks like MLRegTest reveal persistent struggles with formal rule learning ...

Past psychology and behavioral science studies have identified various ways in which people's acquisition of new knowledge can be disrupted. One of these, known as interference, occurs when humans are ...

A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the ...

In 2026, AI research is moving from simply scaling models toward probing their fundamental limits, with benchmarks like MLRegTest revealing gaps in logical generalization and causal reasoning.

Researchers use statistical physics and "toy models" to explain how neural networks avoid overfitting and stabilize learning in high-dimensional spaces.